automatic speech recognition
英 [ˌɔːtəˈmætɪk spiːtʃ ˌrekəɡˈnɪʃn]
美 [ˌɔːtəˈmætɪk spiːtʃ ˌrekəɡˈnɪʃn]
网络 自动语音识别; 自动语音识别系统; 语音识别技术; 自动语音识别技术; 语音识别
双语例句
- For more information about automatic speech recognition and directory lookups, see understanding automatic speech recognition directory lookups.
有关自动语音识别和目录查找的详细信息，请参阅了解自动语音识别目录查找。 - A Study on Noisy Robust Problems in Automatic Speech Recognition by Using Model Compensation
语音识别中基于模型补偿的噪声鲁棒性问题研究 - Thirdly, the application of Artificial Neural Networks ( ANN) to Automatic Speech Recognition ( ASR) is investigated in this thesis.
第三步，基于自动语音识别（ASR）的原理和过程，结合人工神经网络（ANN）的建模理论和特点，主要研究了人工神经网络在语音识别中的应用问题。 - Application of HMM in Automatic Speech Recognition System
HMM在语音识别系统中的应用 - Since HMM was introduced at the end of 1960, it has been applied to the connected, speaker-independent, automatic speech recognition with the advantage of modeling various patterns.
在1960年末被提出的HMM模型，已经被应用的连续的和演讲者无关的自动演讲识别中。 - The paper describes a Chinese spoken dialogue system about real-time stock information, which integrates automatic speech recognition ( ASR), natural language understanding ( NLU), dialogue management and speech generator.
介绍了一个用于股票实时行情查询的口语化的人机对话系统，该系统集成了语音识别、语言理解、对话控制等技术。 - In this paper, the real-time realization of Automatic Speech Recognition ( ASR) technology on common platform is investigated.
本文主要研究普通计算环境下语音自动识别（ASR）技术的软件实时实现。 - Automatic Speech Recognition ( ASR) under noisy environment is still a challenging problem.
噪声情况下的语音识别是个挑战性的问题。 - Now automatic speech recognition ( ASR) is not a simplex signal processing. The natural language processing is more and more regarded in Chinese ASR.
汉语语音识别的研究越来越重视与语言处理的结合，语音识别已经不是单纯的语音信号处理。 - HMM models are widely used in the automatic speech recognition system to segment text-to-speech ( TTS) units in the forced alignment mode.
基于隐尔马可夫模型（HMM）的强制对齐方法被用于文语转换系统（TTS）语音单元边界切分。 - The research on Automatic Speech Recognition has seen great achievement in the passed twenty years.
自动语音识别技术的研究在最近二十年里，取得了很大成就。 - Artificial neural networks approach to automatic speech recognition
自动语音识别研究的人工神经网络方法 - The error rate evaluation is very important in building an Automatic Speech Recognition ( ASR) system. The conventional algorithm for Word Error Rate ( WER) evaluation is based on the minimum error rate.
在建立语音识别系统的过程中错误率评估起着非常重要的作用，传统的词错误率算法仅仅是基于最小错误率，具有显著的缺陷，因而不能准确评估系统的错误率。 - In practical automatic speech recognition systems, out-of-vocabulary ( OOV) utterance detection and rejection represent an important part.
在自动语音识别系统的实际应用中，词表外（Out-of-Vocabulary，OOV）语音的检测与拒识非常重要。 - Techniques for Automatic Speech Recognition in Car Noise
汽车噪声中自动语音的识别技术 - Improved Word Error Rate evaluation algorithm for automatic speech recognition
一种改进的语音识别词错误率评估算法 - Introduced most of related research fields such as ASR ( Automatic Speech Recognition), NLP ( Nature Language Process) and HCI ( Human-Computer Interaction), and focused on the analysis of the architecture and framework of multimodal interaction formulated by W3C.
全面介绍了本研究所涉及的各学科领域，包括语音识别、自然语言理解、人机交互，着重分析了W3C组织制订的多通道交互的技术框架和示范应用。 - Results show that ANN has a higher recognition rate and potential advantages in automatic speech recognition.
研究结果表明，神经网络识别方法有较高的识别率和独特的应用优势。 - SONAR both supports Automatic Speaker Recognition and Automatic Speech Recognition.
SONAR可同时支持说话人识别与语音识别。 - Environmental robustness is a very important issue in the field of automatic speech recognition ( ASR) research.
环境鲁棒性是语音识别系统的一个很重要的问题。 - This paper addresses a kind of method of automatic speech recognition in the presence of interfering noise: Parallel Model Combination ( PMC).
重点研究一个在噪声环境下的语音识别算法&并行模型组合方法（PMC），详细论述了其原理以及在噪声环境下的语音识别中的应用。 - The common method adopted to solve this problem is to establish a language model using automatic speech recognition technology to recognize the pronunciation under test and evaluate the pronunciation capabilities.
常用的方法是采用自动语音识别技术建立语言模型，对待测语音进行识别，根据识别系数来对发音的水平进行评估。 - Statistical Language Model ( SLM) is fundamental to many natural language applications like automatic speech recognition, statistical machine translation, and Asian language text input.
统计语言模型（StatisticalLanguageModel：SLM）在语音识别、统计机器翻译、亚洲语言文本输入等自然语言处理应用中都有重要的应用。 - Automatic speech recognition is essentially a problem of pattern multi-class classification, so the SVM classifier which is well adapted to high-dimensional classification problems is applied in the speech recognition quickly.
语音识别是典型的多类分类问题，因此，善于解决高维分类问题的支持向量机很快被应用到语音识别中。 - The research of automatic speech recognition technology is an important topic in pattern recognition field, and also plays an important role in the actual application.
自动语音识别技术研究是模式识别中重要课题之一，它在实际应用中同样有着重要的作用。 - Speech recognition technology is also called Automatic Speech Recognition ( ASR), its aim is to convert the human words into signals which the computer can identify.
语音识别技术，也被称为自动语音识别（AutomaticSpeechRecognition，ASR），其目标是将人类语音中的词汇内容转换为计算机可读的输入。 - In this context, this thesis focuses on discriminative training of acoustic models and its application in automatic speech recognition.
在这一背景下，本文围绕声学模型区分性训练及其在自动语音识别中的应用，进行了较系统而深入的研究。 - Practical research of automatic speech recognition system is a main direction of speech recognition in recent years. With the rapid development of computer technology and communication technology, speech recognition system turns to embedded device from PC platform.
自动语音识别系统的实用化研究是近几年语音识别研究的一个主要方向，随着计算机技术和通信技术的快速发展，语音识别系统大量地从实验室的PC平台转向了嵌入式设备中。 - This thesis is focused on the research topic of noise-robust front-end of automatic speech recognition ( ASR). As we all know, the ultimate purpose of speech recognition is to make the computer understand human spontaneous language.
本文主要研究的是自动语音识别中的前端噪声鲁棒性问题。众所周知，语音识别的根本目的就是使机器能够听懂人类的语言。 - We know that the human ear can do good job in a noisy environment, this show that human speech perception and recognition capabilities and noise robustness better than all the automatic speech recognition system.
我们知道人耳在噪声环境中也能够很好的工作，由此可见，人类对语音感知和识别的能力及其噪声鲁棒性强于迄今为止的任何自动语音识别系统。